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Disturbances in the myocardial extracellular volume fraction (ECV), such as diffuse or focal myocardial fibrosis or edema, are hallmarks of heart disease. Diffuse ECV changes are difficult to assess or quantify with cardiovascular magnetic resonance (CMR) using conventional late gadolinium enhancement (LGE), or pre- or post-contrast T1-mapping alone. ECV measurement circumvents factors that confound T1-weighted images or T1-maps, and has been shown to correlate well with diffuse myocardial fibrosis. The goal of this study was to develop and evaluate an automated method for producing a pixel-wise map of ECV that would be adequately robust for clinical work flow. ECV maps were automatically generated from T1-maps acquired pre- and post-contrast calibrated by blood hematocrit. The algorithm incorporates correction of respiratory motion that occurs due to insufficient breath-holding and due to misregistration between breath-holds, as well as automated identification of the blood pool. Images were visually scored on a 5-point scale from non-diagnostic (1) to excellent (5). The quality score of ECV maps was 4.23 ± 0.83 (m ± SD), scored for n = 600 maps from 338 patients with 83% either excellent or good. Co-registration of the pre-and post-contrast images improved the image quality for ECV maps in 81% of the cases. ECV of normal myocardium was 25.4 ± 2.5% (m ± SD) using motion correction and co-registration values and was 31.5 ± 8.7% without motion correction and co-registration. Fully automated motion correction and co-registration of breath-holds significantly improve the quality of ECV maps, thus making the generation of ECV-maps feasible for clinical work flow.

Quantification of myocardial blood flow requires knowledge of the amount of contrast agent in the myocardial tissue and the arterial input function (AIF) driving the delivery of this contrast agent. Accurate quantification is challenged by the lack of linearity between the measured signal and contrast agent concentration. This work characterizes sources of non-linearity and presents a systematic approach to accurate measurements of contrast agent concentration in both blood and myocardium. A dual sequence approach with separate pulse sequences for AIF and myocardial tissue allowed separate optimization of parameters for blood and myocardium. A systems approach to the overall design was taken to achieve linearity between signal and contrast agent concentration. Conversion of signal intensity values to contrast agent concentration was achieved through a combination of surface coil sensitivity correction, Bloch simulation based look-up table correction, and in the case of the AIF measurement, correction of T2* losses. Validation of signal correction was performed in phantoms, and values for peak AIF concentration and myocardial flow are provided for 29 normal subjects for rest and adenosine stress. For phantoms, the measured fits were within 5% for both AIF and myocardium. In healthy volunteers the peak [Gd] was 3.5 ± 1.2 for stress and 4.4 ± 1.2 mmol/L for rest. The T2* in the left ventricle blood pool at peak AIF was approximately 10 ms. The peak-to-valley ratio was 5.6 for the raw signal intensities without correction, and was 8.3 for the look-up-table (LUT) corrected AIF which represents approximately 48% correction. Without T2* correction the myocardial blood flow estimates are overestimated by approximately 10%. The signal-to-noise ratio of the myocardial signal at peak enhancement (1.5 T) was 17.7 ± 6.6 at stress and the peak [Gd] was 0.49 ± 0.15 mmol/L. The estimated perfusion flow was 3.9 ± 0.38 and 1.03 ± 0.19 ml/min/g using the BTEX model and 3.4 ± 0.39 and 0.95 ± 0.16 using a Fermi model, for stress and rest, respectively. A dual sequence for myocardial perfusion cardiovascular magnetic resonance and AIF measurement has been optimized for quantification of myocardial blood flow. A validation in phantoms was performed to confirm that the signal conversion to gadolinium concentration was linear. The proposed sequence was integrated with a fully automatic in-line solution for pixel-wise mapping of myocardial blood flow and evaluated in adenosine stress and rest studies on N = 29 normal healthy subjects. Reliable perfusion mapping was demonstrated and produced estimates with low variability.

Parametric mapping techniques provide a non-invasive tool for quantifying tissue alterations in myocardial disease in those eligible for cardiovascular magnetic resonance (CMR). Parametric mapping with CMR now permits the routine spatial visualization and quantification of changes in myocardial composition based on changes in T1, T2, and T2*(star) relaxation times and extracellular volume (ECV). These changes include specific disease pathways related to mainly intracellular disturbances of the cardiomyocyte (e.g., iron overload, or glycosphingolipid accumulation in Anderson-Fabry disease); extracellular disturbances in the myocardial interstitium (e.g., myocardial fibrosis or cardiac amyloidosis from accumulation of collagen or amyloid proteins, respectively); or both (myocardial edema with increased intracellular and/or extracellular water). Parametric mapping promises improvements in patient care through advances in quantitative diagnostics, inter- and intra-patient comparability, and relatedly improvements in treatment. There is a multitude of technical approaches and potential applications. This document provides a summary of the existing evidence for the clinical value of parametric mapping in the heart as of mid 2017, and gives recommendations for practical use in different clinical scenarios for scientists, clinicians, and CMR manufacturers.

IntroductionChronic kidney disease (CKD) affects 1 in 10 people worldwide and can progress towards kidney failure, which is best predicted by the severity of kidney fibrosis. Currently, kidney fibrosis can only be detected by invasive kidney biopsy which carries procedural risks with limitations on repeat testing. MRI techniques have emerged as potential surrogate markers for kidney fibrosis, though data remain limited. To date, no studies have examined postgadolinium contrast T1 mapping in kidney fibrosis despite its proven utility in assessing myocardial fibrosis. This study aims to develop a multiparametric MRI biomarker including postcontrast imaging to quantify kidney fibrosis in individuals with CKD.Methods and analysisIn this observational cohort study, a control group of 20 healthy adult volunteers will establish healthy kidney MRI parameters. Two adult non-dialysis CKD cohorts (each n=24) who have undergone kidney biopsy within the last month will derive and validate the MRI models, respectively. Tubulointerstitial fibrosis on kidney biopsy will be assessed by Masson trichrome staining and quantified based on the percentage of cortex affected by blinded pathologists. All participants will undergo a single multiparametric kidney MRI including kidney volumetry, T1 mapping (pre-low-dose and post-low-dose contrast), T2 mapping, T2* mapping, diffusion weighted imaging and phase-contrast MRI of renal artery flow. The primary outcome will be the association between a composite multiparametric MRI marker and tubulointerstitial fibrosis with a minimum variance of 50%. The association between the multiparametric MRI marker and individual MRI variables, and tubulointerstitial fibrosis, estimated glomerular filtration rate and albuminuria will also be studied.Ethics and disseminationEthics approval has been obtained by the Northern Sydney Local Health District Human Research Ethics Committee (2022/ETH00972). Results will be disseminated in relevant peer-reviewed journals and presented at academic conferences.Trial registration numberACTRN12622000855729p (Pre-results).

Rapid innovations in cardiovascular magnetic resonance (CMR) now permit the routine acquisition of quantitative measures of myocardial and blood T1 which are key tissue characteristics. These capabilities introduce a new frontier in cardiology, enabling the practitioner/investigator to quantify biologically important myocardial properties that otherwise can be difficult to ascertain clinically. CMR may be able to track biologically important changes in the myocardium by: a) native T1 that reflects myocardial disease involving the myocyte and interstitium without use of gadolinium based contrast agents (GBCA), or b) the extracellular volume fraction (ECV)–a direct GBCA-based measurement of the size of the extracellular space, reflecting interstitial disease. The latter technique attempts to dichotomize the myocardium into its cellular and interstitial components with estimates expressed as volume fractions. This document provides recommendations for clinical and research T1 and ECV measurement, based on published evidence when available and expert consensus when not. We address site preparation, scan type, scan planning and acquisition, quality control, visualisation and analysis, technical development. We also address controversies in the field. While ECV and native T1 mapping appear destined to affect clinical decision making, they lack multi-centre application and face significant challenges, which demand a community-wide approach among stakeholders. At present, ECV and native T1 mapping appear sufficiently robust for many diseases; yet more research is required before a large-scale application for clinical decision-making can be recommended.

Diffuse myocardial fibrosis, and to a lesser extent global myocardial edema, are important processes in heart disease which are difficult to assess or quantify with cardiovascular magnetic resonance (CMR) using conventional late gadolinium enhancement (LGE) or T1-mapping. Measurement of the myocardial extracellular volume fraction (ECV) circumvents factors that confound T1-weighted images or T1-maps. We hypothesized that quantitative assessment of myocardial ECV would be clinically useful for detecting both focal and diffuse myocardial abnormalities in a variety of common and uncommon heart diseases. A total of 156 subjects were imaged including 62 with normal findings, 33 patients with chronic myocardial infarction (MI), 33 with hypertrophic cardiomyopathy (HCM), 15 with non-ischemic dilated cardiomyopathy (DCM), 7 with acute myocarditis, 4 with cardiac amyloidosis, and 2 with systemic capillary leak syndrome (SCLS). Motion corrected ECV maps were generated automatically from T1-maps acquired pre- and post-contrast calibrated by blood hematocrit. Abnormally-elevated ECV was defined as >2SD from the mean ECV in individuals with normal findings. In HCM the size of regions of LGE was quantified as the region >2 SD from remote. Mean ECV of 62 normal individuals was 25.4 ± 2.5% (m ± SD), normal range 20.4%-30.4%. Mean ECV within the core of chronic myocardial infarctions (without MVO) (N = 33) measured 68.5 ± 8.6% (p < 0.001 vs normal). In HCM, the extent of abnormally elevated ECV correlated to the extent of LGE (r = 0.72, p < 0.001) but had a systematically greater extent by ECV (mean difference 19 ± 7% of slice). Abnormally elevated ECV was identified in 4 of 16 patients with non-ischemic DCM (38.1 ± 1.9% (p < 0.001 vs normal) and LGE in the same slice appeared “normal” in 2 of these 4 patients. Mean ECV values in other disease entities ranged 32-60% for cardiac amyloidosis (N = 4), 40-41% for systemic capillary leak syndrome (N = 2), and 39-56% within abnormal regions affected by myocarditis (N = 7). ECV mapping appears promising to complement LGE imaging in cases of more homogenously diffuse disease. The ability to display ECV maps in units that are physiologically intuitive and may be interpreted on an absolute scale offers the potential for detection of diffuse disease and measurement of the extent and severity of abnormal regions.

Knowledge on sex differences in myocardial perfusion, blood volume (MBV), and extracellular volume (ECV) in healthy individuals is scarce and conflicting. Therefore, this was investigated quantitatively by cardiovascular magnetic resonance (CMR). Healthy volunteers ( n  = 41, 51% female) underwent CMR at 1.5 T. Quantitative MBV [%] and perfusion [ml/min/g] maps were acquired during adenosine stress and at rest following an intravenous contrast bolus (0.05 mmol/kg, gadobutrol). Native T1 maps were acquired before and during adenosine stress, and after contrast (0.2 mmol/kg) at rest and during adenosine stress, rendering rest and stress ECV maps. Compared to males, females had higher perfusion, ECV, and MBV at stress, and perfusion and ECV at rest (p < 0.01 for all). Multivariate linear regression revealed that sex and MBV were associated with perfusion (sex beta −0.31, p = 0.03; MBV beta −0.37, p = 0.01, model R 2  = 0.29, p < 0.01) while sex and hematocrit were associated with ECV (sex beta −0.33, p = 0.03; hematocrit beta −0.48, p < 0.01, model R 2  = 0.54, p < 0.001). Myocardial perfusion, MBV, and ECV are higher in female healthy volunteers compared to males. Sex is an independent contributor to perfusion and ECV, beyond other physiological factors that differ between the sexes. These findings provide mechanistic insight into sex differences in myocardial physiology.

Electrocardiographic (ECG) Heart Age conveying cardiovascular risk has been estimated by both Bayesian and artificial intelligence approaches. We hypothesised that explainable measures from the 10-s 12-lead ECG could successfully predict Bayesian 5-min ECG Heart Age. Advanced analysis was performed on ECGs from healthy subjects and patients with cardiovascular risk or proven heart disease. Regression models were used to predict patients’ Bayesian 5-min ECG Heart Ages from their standard, resting 10-s 12-lead ECGs. The difference between 5-min and 10-s ECG Heart Ages were analyzed, as were the differences between 10-s ECG Heart Age and the chronological age (the Heart Age Gap). In total, 2,771 subjects were included (n = 1682 healthy volunteers, n = 305 with cardiovascular risk factors, n = 784 with cardiovascular disease). Overall, 10-s Heart Age showed strong agreement with the 5-min Heart Age (R 2  = 0.94, p  < 0.001, mean ± SD bias 0.0 ± 5.1 years). The Heart Age Gap was 0.0 ± 5.7 years in healthy individuals, 7.4 ± 7.3 years in subjects with cardiovascular risk factors ( p  < 0.001), and 14.3 ± 9.2 years in patients with cardiovascular disease ( p  < 0.001). Heart Age can be accurately estimated from a 10-s 12-lead ECG in a transparent and explainable fashion based on known ECG measures, without deep neural network-type artificial intelligence techniques. The Heart Age Gap increases markedly with cardiovascular risk and disease.

Background Body position can be optimized for pulmonary ventilation/perfusion matching during surgery and intensive care. However, positional effects upon distribution of pulmonary blood flow and vascular distensibility measured as the pulmonary blood volume variation have not been quantitatively characterized. In order to explore the potential clinical utility of body position as a modulator of pulmonary hemodynamics, we aimed to characterize gravitational effects upon distribution of pulmonary blood flow, pulmonary vascular distension, and pulmonary vascular distensibility. Methods Healthy subjects ( n  = 10) underwent phase contrast cardiovascular magnetic resonance (CMR) pulmonary artery and vein flow measurements in the supine, prone, and right/left lateral decubitus positions. For each lung, blood volume variation was calculated by subtracting venous from arterial flow per time frame. Results Body position did not change cardiac output ( p  = 0.84). There was no difference in blood flow between the superior and inferior pulmonary veins in the supine ( p  = 0.92) or prone body positions ( p  = 0.43). Compared to supine, pulmonary blood flow increased to the dependent lung in the lateral positions (16–33%, p  = 0.002 for both). Venous but not arterial cross-sectional vessel area increased in both lungs when dependent compared to when non-dependent in the lateral positions (22–27%, p  ≤ 0.01 for both). In contrast, compared to supine, distensibility increased in the non-dependent lung in the lateral positions (68–113%, p = 0.002 for both). Conclusions CMR demonstrates that in the lateral position, there is a shift in blood flow distribution, and venous but not arterial blood volume, from the non-dependent to the dependent lung. The non-dependent lung has a sizable pulmonary vascular distensibility reserve, possibly related to left atrial pressure. These results support the physiological basis for positioning patients with unilateral pulmonary pathology with the “good lung down” in the context of intensive care. Future studies are warranted to evaluate the diagnostic potential of these physiological insights into pulmonary hemodynamics, particularly in the context of non-invasively characterizing pulmonary hypertension.

Background Commercially available software for cardiovascular image analysis often has limited functionality and frequently lacks the careful validation that is required for clinical studies. We have already implemented a cardiovascular image analysis software package and released it as freeware for the research community. However, it was distributed as a stand-alone application and other researchers could not extend it by writing their own custom image analysis algorithms. We believe that the work required to make a clinically applicable prototype can be reduced by making the software extensible, so that researchers can develop their own modules or improvements. Such an initiative might then serve as a bridge between image analysis research and cardiovascular research. The aim of this article is therefore to present the design and validation of a cardiovascular image analysis software package (Segment) and to announce its release in a source code format. Results Segment can be used for image analysis in magnetic resonance imaging (MRI), computed tomography (CT), single photon emission computed tomography (SPECT) and positron emission tomography (PET). Some of its main features include loading of DICOM images from all major scanner vendors, simultaneous display of multiple image stacks and plane intersections, automated segmentation of the left ventricle, quantification of MRI flow, tools for manual and general object segmentation, quantitative regional wall motion analysis, myocardial viability analysis and image fusion tools. Here we present an overview of the validation results and validation procedures for the functionality of the software. We describe a technique to ensure continued accuracy and validity of the software by implementing and using a test script that tests the functionality of the software and validates the output. The software has been made freely available for research purposes in a source code format on the project home page http://segment.heiberg.se . Conclusions Segment is a well-validated comprehensive software package for cardiovascular image analysis. It is freely available for research purposes provided that relevant original research publications related to the software are cited.